An Evaluation of Machine Learning Classifiers for Prediction of Alzheimer's Disease, Mild Cognitive Impairment and Normal Cognition

Abstract

Dementia has a negative impact on global healthcare that has become a serious concern worldwide. The most common cause, Alzheimer's disease, underlies the majority of dementia. The identification and accurate prediction of Alzheimer's disease in its initial stage is most critical, of which has several important practical applications. However, a reliable diagnosis remains a challenging task and requires a combination of methods based on important clinical information. Developing computer-aided diagnosis systems to support early Alzheimer's disease detection is essential for effective treatment planning. In this study, a nationwide cohort dataset, the Korean Brain Aging Study for the Early diagnosis and prediction of Alzheimer's disease is classified by using eight state-of-the-art supervised machine learning algorithms namely Support Vector Machine, Naive Bayes, XGBoost, Decision Tree, Logistic Regression, Random Forest, Bagging and AdaBoost. The best performing model appeared to be the XGBoost classifier yielding an accuracy of 82.09%. Thus, the present research shows that the application of the machine learning model to the KBASE dataset will offer an efficient clinical classification of cognitively normal control individuals, mild cognitive impairment and Alzheimer's disease patients and provides a framework for clinical decision systems.